# JointMap: Joint Query Intent Understanding For Modeling Intent Hierarchies in E-commerce Search

arXiv: https://arxiv.org/abs/2005.13783 · HTML: https://mlsanity.com/paper/2005.13783/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 May 2020.

## Built on by (most cited first, 5 of 5)

- [EcomGPT: Instruction-tuning Large Language Models with Chain-of-Task Tasks for E-commerce](https://mlsanity.com/paper/2308.06966/) (2023)
- [Knowledge-Decoupled Synergetic Learning: An MLLM based Collaborative Approach to Few-shot Multimodal Dialogue Intention Recognition](https://mlsanity.com/paper/2503.04201/) (2025)
- [REIC: RAG-Enhanced Intent Classification at Scale](https://mlsanity.com/paper/2506.00210/) (2025)
- [Towards Better Query Classification with Multi-Expert Knowledge Condensation in JD Ads Search](https://mlsanity.com/paper/2308.01098/) (2023)
- [Identifying Shopping Intent in Product QA for Proactive Recommendations](https://mlsanity.com/paper/2404.06017/) (2024)

## Abstract (arXiv)

An accurate understanding of a user's query intent can help improve the performance of downstream tasks such as query scoping and ranking. In the e-commerce domain, recent work in query understanding focuses on the query to product-category mapping. But, a small yet significant percentage of queries (in our website 1.5% or 33M queries in 2019) have non-commercial intent associated with them. These intents are usually associated with non-commercial information seeking needs such as discounts, store hours, installation guides, etc. In this paper, we introduce Joint Query Intent Understanding (JointMap), a deep learning model to simultaneously learn two different high-level user intent tasks: 1) identifying a query's commercial vs. non-commercial intent, and 2) associating a set of relevant product categories in taxonomy to a product query. JointMap model works by leveraging the transfer bias that exists between these two related tasks through a joint-learning process. As curating a labeled data set for these tasks can be expensive and time-consuming, we propose a distant supervision approach in conjunction with an active learning model to generate high-quality training data sets. To demonstrate the effectiveness of JointMap, we use search queries collected from a large commercial website. Our results show that JointMap significantly improves both "commercial vs. non-commercial" intent prediction and product category mapping by 2.3% and 10% on average over state-of-the-art deep learning methods. Our findings suggest a promising direction to model the intent hierarchies in an e-commerce search engine.